Apparatus and methods for the storage of overlapping regions of imaging data for the generation of optimized stitched images

ABSTRACT

Apparatus and methods for stitching images, or re-stitching previously stitched images. Specifically, the disclosed systems in one implementation save stitching information and/or original overlap source data during an original stitching process. During subsequent retrieval, rendering, and/or display of the stitched images, the originally stitched image can be flexibly augmented, and/or re-stitched to improve the original stitch quality. Practical applications of the disclosed solutions enable, among other things, a user to create and stitch a wide field of view (FOV) panorama from multiple source images on a device with limited processing capability (such as a mobile phone or other capture device). Moreover, post-processing stitching allows for the user to convert from one image projection to another without fidelity loss (or with an acceptable level of loss).

RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.15/289,851 filed Oct. 10, 2016 and entitled “Apparatus and Methods forthe Optimal Stitch Zone Calculation of a Generated Projection of aSpherical Image”, which is incorporated herein by reference in itsentirety. This application is also related to U.S. patent applicationSer. No. 15/234,869 filed Aug. 11, 2016 and entitled “EquatorialStitching of Hemispherical Images in a Spherical Image Capture System”,which claims the benefit of priority to U.S. Provisional PatentApplication Ser. No. 62/204,290 filed on Aug. 12, 2015, each of theforegoing being incorporated herein by reference in its entirety.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND OF THE DISCLOSURE

Field of the Disclosure

The present disclosure relates generally to video image processing andin one exemplary aspect, to methods and apparatus for the storage and/orsubsequent provision of overlapping regions of imaging data for thegeneration of optimized stitched images.

Description of Related Art

Spherical images are typically obtained by capturing multiple imageswith overlapping fields of view from different cameras and combining(“stitching”) these images together in order to provide atwo-dimensional projection. Conventional stitching algorithms may resultin undesirable artifacts, including around the stitch lines, due toimperfections in the stitching process.

Different electronics devices may have different mobility, computationalcapabilities, display capabilities, power limitations, and/or otheroperational considerations. For example, a consumer device such as amobile phone or multi-camera capture device may be convenient to capturesource photographs during an outdoor activity, and/or quickly view aroughly stitched image, however it may lack the computational power toperform high quality stitching. Similarly, while a laptop or towercomputer may have more than adequate compute power to perform highquality stitching, they are poorly suited to outdoor activities.

Unfortunately, existing image capture techniques and rendering formats“fix” a stitched image to a particular quality. For example, when amobile device quickly stitches an image from multiple source images, theresulting stitched image cannot be re-stitched on a more capablemachine; it is locked into the quality of the mobile device (which maybe relatively poor).

Additionally, storing post-stitch data with the original source imagesmay not always be feasible due to e.g., storage considerations.

Furthermore, observable quality is highly subjective and the user may bewilling to accept a quickly stitched version with limited augmentationto a higher stitch quality. In some cases, this may even be limited tospatial regions of the same image or time regions of the same videostream.

Moreover, prior art techniques assume a particular projection (e.g.,equirectangular (cylindrical), cubic, octahedral, and/or sphericalprojection) for stitching images (e.g., for so-called virtual reality(VR) content). Once an image has been stitched according to a firstprojection, the image cannot be changed to a different projection.Instead, if a different projection is desired, the source images must bere-stitched from scratch according to the new projection.

To these ends, techniques are needed to improve upon these conventionalstitching algorithms. Moreover, improvements are needed to improvecompression efficiencies associated with the transmission and storage ofstitched images in order to, inter alia, more efficiently leverage thecapabilities of rendering platforms and/or viewing applications,regardless of the original source images.

SUMMARY

The present disclosure satisfies the foregoing needs by providing, interalia, methods and apparatus for stitching and/or re-stitching images,including at different qualities.

In one aspect, an apparatus configured to stitch source images accordingto a first stitching quality is disclosed. In one embodiment, theapparatus includes: two or more cameras characterized by two or morecorresponding fields of view (FOVs); wherein the two or morecorresponding FOVs are characterized by at least one overlapping region;a processor; and a non-transitory computer readable medium. In oneexemplary implementation, the non-transitory computer readable mediumincludes one or more instructions which when executed by the processor,cause the apparatus to: obtain two or more images from the two or morecameras; identify the at least one overlapping region of the obtainedtwo or more images; post-process the obtained two or more images tocreate a post-processed image; and store the post-processed image andone or more information associated with the identified at least oneoverlapping region.

In one variant, the identified at least one overlapping region of theobtained two or more images are identified based on a physicalorientation of the two or more cameras and the two or more FOVs.

In another variant, the identified at least one overlapping region ofthe obtained two or more images are identified based on one or moreshared features detected within the obtained two or more images.

In a third variant, the post-process includes a cut-and-feather stitchof the obtained two or more images.

In a fourth variant, the post-process includes a depth based stitch ofthe obtained two or more images.

In a fifth variant, the stored one or more information includesstitching confidence metrics.

In a sixth variant, the stored one or more information includes originalpixel information associated with the at least one overlapping region.

In a seventh variant, the two or more image capture components include afirst camera facing in a front orientation, and a second camera facingin a rear orientation. In one exemplary variant, the first and secondcamera are characterized by a corresponding first and secondhyper-hemispherical FOVs.

A method for stitching source images according to a first stitchingquality is also disclosed. In one embodiment, the method includes:obtaining two or more source images from two or more cameras;identifying at least one overlapping region of the obtained two or moresource images; post-processing the obtained two or more source images tocreate a post-processed image; and storing the post-processed image andone or more information associated with the identified at least oneoverlapping region.

In one variant, the method includes displaying at least a portion of thepost-processed image. In one exemplary variant, the post-processingfurther includes determining a projection for the displaying. In somesuch cases, the method may further include stitching at least a portionof the post-processed image based on the determined projection.

In another variant, the post-processing further includes determining aviewport to display. In one exemplary variant, the method furtherincludes stitching at least a portion of the post-processed image basedon the determined viewport.

In a third variant, the post-processing further includes determining oneor more device resource limitations. In one such variant, the methodfurther includes stitching at least a portion of the post-processedimage to a reduced degree or extent based on the determined one or moredevice resource limitations.

An apparatus configured to re-stitch an image characterized by a firststitching quality to a second stitching quality is further disclosed. Inone exemplary embodiment, the apparatus includes: a network interface; aprocessor; and a non-transitory computer readable medium including oneor more instructions which when executed by the processor, cause theapparatus to: obtain at least a portion of the image characterized bythe first stitching quality; determine a region of the at least theportion of the image to be re-stitched to a second stitching quality;obtain one or more information associated with the determined region;and re-stitch the region at the second stitching quality.

In one variant, the determined region corresponds to at least oneoverlapping region of two or more source images. In one such exemplaryvariant, the obtained one or more information associated with thedetermined region includes at least one of: (i) stitching confidencemetrics, and (ii) original capture information from the two or moresource images.

In a further aspect, a computer-readable storage apparatus is disclosed.In one embodiment, the apparatus includes a storage medium with aplurality of instructions which are configured to, when executed on aprocessing apparatus: obtain two or more source images from two or morecameras; identify at least one overlapping region of the obtained two ormore source images; post-process the obtained two or more source imagesto create a post-processed image; and store the post-processed image andone or more information associated with the identified at least oneoverlapping region.

In a further aspect, an integrated circuit apparatus is disclosed. Inone embodiment, the integrated circuit apparatus includes one or moreapplication-specific integrated circuits (ASICs) which include circuitryconfigured to conduct image data post-processing, such as according tothe method referenced supra.

Other features and advantages of the present disclosure will immediatelybe recognized by persons of ordinary skill in the art with reference tothe attached drawings and detailed description of exemplaryimplementations as given below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one exemplary spherical camera system, inaccordance with the principles of the present disclosure.

FIG. 2 is a logical flow diagram of a method for stitching source imagesaccording to a first stitching quality, in accordance with theprinciples of the present disclosure.

FIG. 3 is a logical representation of one exemplary file format usedwith the exemplary camera system of FIG. 1, illustrative of theprinciples of the present disclosure.

FIG. 4 is a logical representation of one exemplary file formatrepresenting a stitching of the exemplary file format of FIG. 3,illustrative of the principles of the present disclosure.

FIG. 5 is a logical flow diagram of a method for re-stitching apreviously stitched image according to a second stitching quality, inaccordance with the principles of the present disclosure.

FIG. 6 is a logical representation of an exemplary initial stitching ofa spherical projection that can be re-stitched to two cylindricalprojections, illustrative of the principles of the present disclosure.

FIG. 7 is a logical representation of a method for converting imagesthat have been stitched in a polar view to a more desirable projectionfor viewing, in accordance with the principles of the presentdisclosure.

FIG. 8 is a block diagram of an exemplary implementation of a computingdevice, useful in performing the methodologies described herein.

All Figures disclosed herein are ©Copyright 2017 GoPro, Inc. All rightsreserved.

DETAILED DESCRIPTION

Implementations of the present technology will now be described indetail with reference to the drawings, which are provided asillustrative examples and species of broader genera so as to enablethose skilled in the art to practice the technology. Notably, thefigures and examples below are not meant to limit the scope of thepresent disclosure to any single implementation or implementation, butother implementations are possible by way of interchange of,substitution of, or combination with some or all of the described orillustrated elements. Wherever convenient, the same reference numberswill be used throughout the drawings to refer to same or like parts.

Moreover, while implementations described herein are primarily discussedin the context of spherical images that are captured using a sphericalcamera system having two (2) cameras (e.g., a front-facing and arear-facing camera), it is readily appreciated that the principlesdescribed herein can be equally applied to other camera configurations.For example, when obtaining panoramic (e.g., 360°) content, three ormore images from three or more cameras may be combined (stitched).

Additionally, while primarily discussed in the context of cameraconfigurations in which each of the centers of view for the respectivecameras reside on a given two-dimensional plane, it is readilyappreciated that one or more of these cameras can reside such that theircenter of view is focused at an azimuthal angle (e.g., at 45°), withrespect to the given two-dimensional plane for other one(s) of thecameras.

Those of ordinary skill in the related arts will also readily appreciatethat symmetric and asymmetric camera configurations can be substitutedwith equivalent success. For example, a symmetric dual camera system (aJanus configuration) may have fisheye lenses that provide a field ofview (FOV) that is greater than 180°. In asymmetric implementations, thecameras may have different FOV angles; e.g., a higher resolution 195°front-facing camera, and a lower resolution 245° rear-facing camera.Such implementations may be useful to store front and back imagesaccording to a common format size, while still providing higherresolution for objects within the field of interest (e.g., the frontcamera's perspective).

While the present disclosure is presented within the context of staticphotography, artisans of ordinary skill in the related arts will readilyappreciate that the various principles described herein may be appliedto a wide range of imaging applications, including e.g., video capture,video rendering, virtual reality, augmented reality (AR). For example, apanorama image can be generated from a video capture while rotating acamera (e.g., stitching together the individual frames in time asdifferent fields of view (FOV)). Similarly, source images may bedynamically stitched together during a video playback (e.g., for virtualreality (VR), augmented reality (AR) applications, mixed reality,augmented virtuality, and/or other hybridized realities).

These and other variations would be readily apparent to one of ordinaryskill given the contents of the present disclosure.

Overview

Various aspects of the present disclosure are directed to improvedsolutions for stitching, as well as re-stitching previously stitchedimages. Specifically, during an original stitching process, in additionto storing the resulting stitched image, the disclosed systems also savestitching information and/or original overlap source data. Duringsubsequent retrieval, rendering, and/or display of the stitched images,the originally stitched image can be flexibly augmented, and/orre-stitched to improve the original stitch quality.

Practical applications of the disclosed solutions enable a user to,inter alia, create and stitch a wide field of view (FOV) panorama frommultiple source images on a device with limited processing capability(such as a mobile phone or other capture device). The convenience ofbeing able to immediately view the panorama (at the time of capture) maybe more important than the lower quality of the initial stitching.Subsequently thereafter, once the user has access to more capablerendering (e.g., at home or via data network access), the quicklystitched image may be re-stitched at a higher quality.

In some cases, the re-stitching may be selective; for example, variousportions of the image can be re-stitched at higher quality, whereasother portions of the image may retain the lower quality stitching. Suchtechniques may be especially useful in applications that do not renderthe entire stitched image at once; for example, virtual reality (VR) andaugmented reality (AR) only render the portion of the image that theviewer is “looking” at (i.e., the viewport). This is especially usefulin a resource limited device e.g. smartphone which does not have toperform re-stitching on the entire image. More directly, the degreeand/or extent of stitching may be limited based on device limitationse.g., processing or memory resources. In related embodiments, variousframes of a video may be re-stitched at higher quality, whereas otherportions of the video may be left at the original lower qualitystitching. Selectivity may be based on the quality of the originalstitching; in other words, where the original stitch was sufficient, theimprovement of re-stitching may not be worth the additional processingeffort.

Selectivity may also be based on the application; for example, in arapidly moving video sequence, the user is unlikely to perceive anydifference in image quality, however in slow moving video, stitchingartifacts may be much more noticeable. Selectivity may also be based ondistance of the objects from the camera system. Closer objects mayrequire high quality depth based stitching due to greater parallelaxeffects, but farther objects may not require advanced stitching.

Additionally, artisans of ordinary skill in the related arts willreadily appreciate, given this disclosure, that saving the stitching andoverlap region within a common data format enables multiple devices tointelligently request, provide, and/or retrieve only as much informationas is necessary to construct or reconstruct a stitched image for theappropriate application. Accordingly, a first device can retrieve theoriginal image and re-stitch the image according to a first projection(e.g., an equirectangular projection), while a second device canretrieve the original image and re-stitch the image according to adifferent second projection (e.g., a cubic or spherical projection).Moreover, as discussed in greater detail hereinafter, converting fromone projection to another may not always require re-stitching; it may beenough to warp or stretch portions of an existing projection, andselectively re-stitch only where the two projections differ. Forexample, an equirectangular projection can be converted to a sphericalprojection with much less effort by stretching/warping the bulk of theimage; only the poles of the image need to be re-stitched.

Example Image Capturing System—

FIG. 1 illustrates an embodiment of an example spherical camera system100 that may include a first camera 110 capturing a first field of view112 and a second camera 120 capturing a second field of view 122. In oneor more implementations, the cameras 110, 120 may be integrated in aback-to-back configuration in which cameras 110, 120 face oppositedirections. For example, in operation, the first camera 110 may be a“front-facing” camera 110 such that a user may point the first cameratowards an object or scene of interest and the second camera 120 may bea “rear-facing” camera facing in an opposite direction of thefront-facing camera 110. While the designations of front-facing andrear-facing are useful in describing the example processes describedherein, these designations are arbitrary and the camera system 100 mayoperate in any orientation.

The fields of view 112, 122 may each include a hyper-hemispherical fieldof view that captures slightly greater than a 180° range in at least onedirection. Because the respective fields of view 112, 122 arehyper-hemispherical (e.g., greater than 180°), they overlap inoverlapping regions 132, 134 near their respective boundaries. Forexample, the fields of view 112, 122 may overlap by n degrees (e.g.,where n equals 1°, 5°, 10° or other various degrees of field of viewoverlap between, for example, a front-facing and rear-facing camera).These overlapping regions 132, 134 may be used for the stitching ofseparately captured images obtained by the respective cameras 110, 120,as will be described in further detail below. In implementations wherethe respective field of view ranges are equal for each of the firstcamera 110 and the second camera 120, these configurations will bereferred to hereinafter as symmetric lensing configurations.

In some implementations, the first camera 110 may be configured tocapture a one range in at least one direction (e.g., 195°), while thesecond camera 120 may be configured to capture a different range in atleast one direction (e.g., 225°). In other words, the first and secondcameras 110, 120 may capture differing ranges in their respective fieldsof view 112, 122 so long as their fields of view overlap in at least oneoverlapping region 132,134. In implementations where the respectivefield of view ranges differ for each of the first camera 110 and thesecond camera 120, these configurations will be referred to hereinafteras asymmetric lensing configurations.

It will be appreciated that certain camera configurations contain three(or more) cameras; the corresponding field of views for these camerasdon't necessarily have to be hyper-hemispherical (i.e., greater than180°). For example, in an implementation that utilizes three cameras,each of these cameras may capture an image that has a field of view thatis greater than a 120° range in at least one direction, so that theresultant images may be stitched together into a full 360° field ofview. Implementations that utilize three (or more) cameras maycollectively contain either a symmetric lensing configuration or,alternatively, may collectively contain an asymmetric lensingconfiguration. Similarly, where a complete panorama is not required(e.g., less than a full 360° field of view), fewer cameras with reducedview angles can be used with equivalent success.

The number of pixels in a camera sensor and the field of view (FOV) aretypically “fixed” in a camera system and do not change during use.Generally, the manufacturer will design the camera to suit the intendedapplication(s). For instance, an activity camera that is mobile andrugged will have different capture parameters than a cinema-qualitycamera designed for e.g., crane mounts or other bulky steadyingplatforms. Artisans of ordinary skill in the related arts will readilyappreciate that the same number of pixels may be used to capture alarger field of view (FOV) at lower resolution, or a smaller FOV at ahigher resolution. For instance, a ten (10) Megapixel (MP) camera sensorthat is coupled to a 195° FOV lens provides a higher resolution than thesame 10 MP camera sensor used for a 245° FOV lens.

As shown in the configuration of FIG. 1, the overlapping regions 132,134 are fixed and do not change during use. Camera manufacturers maydesign the camera body with larger or smaller overlap regions; a largeroverlap region may be used for better quality image stitching, but canresult in an overall drop in image resolution as a result of a loweramount of pixels per degree of FOV (i.e., a lower number of pixels perdegree of FOV). Conversely, a smaller overlap region may be used forlower quality image stitching, but may result in an overall increase inimage resolution for the captured image.

In other designs, the overlapping regions may be configurable, due tochanges in the camera body and/or lens. Video variants may even be ableto dynamically change overlap regions during an ongoing capture. Forexample, video cameras may have optical lens elements that canphysically change (even during ongoing capture) e.g., a zoom body thatallows the camera to change the focal length. Similarly, static camerasare commonly designed with modular components that can be changed out;for example, different lens attachments can impart different view anglesand/or focal lengths. Some cameras may even be constructed to allowdifferent sensors or may selectively use different sensors withdifferent capture characteristics (e.g., switching between optical andIR sensors, or between higher and lower capture quality sensors).

As a brief aside, artisans of ordinary skill in the related arts willreadily appreciate, given this disclosure, that objects within theoverlap region may be redundantly captured in one or both FOVs.Referring back to FIG. 1, a number of example objects (142, 144, 146,148, 150, 152, 154, 156) are shown to illustrate the potentiallimitations and blind spots of the exemplary camera system. As shown,object 142 shows up only in the first field of view 112, and object 144shows up only in the second field of view 122. Object 146 shows up inboth the first field of view 112 and the second field of view 122;whereas object 148 is in a negligible “blind spot” of both fields ofview (typically within a few inches of the camera). Object 150 ispartially in the overlapping region 132; thus, object 132 is fullyvisible for the first field of view 112, but only partially visible inthe second field of view 122. Additionally, object 150 creates atemporary blind spot: object 152 is completely hidden from both fieldsof view, object 154 is hidden from the first field of view 112, andobject 156 is visible to both fields of view.

When stitching source images from the two (2) fields of view together,the stitching algorithm should ideally reconcile at least any salientdifferences between the two (2) source images. For example, objects 146and 156 of FIG. 1 will appear slightly different between the two (2)source images due to parallax effects. Similarly, object 150 will befully visible in the first source image and only partially visible inthe second source image. Object 154 will only be visible in the secondsource image. Object 152 was present in both source images untiloccluded by object 150; thus, depending on the particular imagingapplication, object 152 (or a portion thereof) may be selectivelyrendered. For example, a video of object 152 passing behind object 150may selectively render from the first field of view to the second fieldof view to more closely mimic expected physics (e.g., a constantvelocity of movement).

As used herein, the term “parallax” refers without limitation to adisplacement or difference in the apparent position of an object viewedalong different lines of sight. Parallax can be mathematically expressedor described with the angle or semi-angle of inclination between thelines of sight. As used herein, the term “perspective” refers to awarping or difference in the apparent dimensions of an object viewedalong a line of sight.

As used herein, the term “redundant” within the context of sourceimages, refers without limitation to pixel information that is found inmultiple source images. Redundant information may be introduced byparallax effects and can be reconciled together to generate stitchedoutput information. In contrast, as used herein, the term “singular”within the context of source images, refers without limitation to pixelinformation that is only found in a single source image. Singularinformation may be caused by blind spots or other occlusions and cannotbe reconciled between images without introducing undesirable artifacts(e.g., translucency, warping, or other undesirable visual effects).

Redundantly captured information can be used in the stitching process toimprove stitching. For example, sophisticated stitching techniques mayattempt to use parallax information to discern distances and/or evaluatedepth of the field of view. Still other techniques may intelligentlyweight or select (to the exclusion of others) singular pixel informationfrom each field of view. However, simple stitching techniques may notdifferentiate between redundant and singular information; e.g., simplestitching may use a relatively simple cut-and-feather scheme, averagingscheme, or other unilaterally applied filtering technique.

Existing prior art stitching techniques generate a stitched image frommultiple source images; however, the information within the overlapregion is not also stored (and is lost). In other words, subsequentmodifications to a file do not have the benefit of the original sourceinformation. For example, a post-processing video editor cannot tellfrom the pixel information whether it was generated from redundant pixelinformation or selected from singular pixel information. The disclosedembodiments of the present disclosure retain such information for pixelswithin the overlap regions, and thus enable flexible post-processing ofstitched images.

Exemplary Capture, Stitching, and Re-Stitching Methodologies—

The processes described herein may be performed by a video processingsystem including at least one processor and a non-transitorycomputer-readable storage apparatus having a storage medium. The storagemedium stores a number of computer-executable instructions thereon, thatwhen executed by the at least one processor, cause the at least oneprocessor to perform the processes described herein. In an embodiment,the video processing system may be partially or wholly implemented inthe camera 100 or may be implemented partially or wholly in an externaldevice (e.g., in a computing device that is separate from the camerasystem 100 that obtained the resultant images). The various projectionmethodologies described herein are useful in, for example, thecompression, storage and/or transmission of this captured video data.

Additionally, the processes and methodologies described herein (orportions thereof) may be performed by dedicated computerized systemlogic, including without limitation, application-specific integratedcircuits (ASICs), field-programmable gate arrays (FPGAs), and/or othertypes of integrated circuits or dedicated computerized logic that may beutilized in addition to, or alternatively from, the aforementionedcomputer-readable storage apparatus.

FIG. 2 illustrates a generalized method for stitching source imagesaccording to a first stitching quality.

At step 202 of the method 200, one or more source images are obtained.In one embodiment, the source images are obtained from multiple camerasthat are oriented with a fixed or otherwise known relationship to oneanother. Common examples of a fixed or known relationship are e.g., aJanus type camera body, a multi-lens body (e.g., a three (3), four (4),five (5), six (6) or more camera body). Certain vehicles, cranes orfilming rigs may have multiple camera or sensor mounts at fixedpositions relative to one another, as may certain military aircraft.While such configurations may vary widely from one shoot to another, forthe duration of use, the cameras do not change with relation to oneanother.

In another embodiment, the source images are obtained from multiplecameras that are oriented according to a dynamic relationship that isidentified based on e.g., sensors within the camera or camera fixture.For example, the camera may have accelerometers, motion detection,and/or location determination systems (e.g., Global positioning system(GPS)) that allows the camera to infer the relative positioning of theFOV for each source image. For example, certain sophisticated camerarigs may have stabilization mechanisms that enable the cameras to moveto a limited degree in order to capture a more stable image. Suchstabilization information may be either directly captured (viaaccelerometer or other motion sensor data), or may be inferred from thecaptured image (as described in greater detail hereinafter). Based onthe relative movements of cameras with respect to one another, theoverlapping region will change in size (e.g., the FOVs may drifttogether or apart relative to one another)

In still other embodiments, the source images are obtained without anypositional information, and the relative positions of the source imagesis inferred from the image data. For example, a user or post-processingengine may be able to identify reference points and/or edges which areshared between the source images. Reference points may be provided withilluminated dots (such as via a laser pointer, or other pointingdevice), reflective tape, or other visible indicia. In some embodiments,the indicia may be non-visible (e.g., infrared, sonic, or othernon-perceptible signaling). Such indicia may be provided in peripheralregions that are of limited interest (and can be edited out withoutaffecting the subject). Similarly, reference edges, lines and/or planesmay be used with equivalent success, by an artisan of ordinary skill inthe related arts, given the contents of the present disclosure.

At step 204 of the method 200, overlapping portions of the one or moresource images and non-overlapping portions are identified. In oneexemplary embodiment, the overlapping and non-overlapping portions ofthe source images are determined according to the fixed physicalparameters of the camera system e.g., based on a known view angle of thelenses and the separation between the lenses, the overlap region can bedescribed as the region of each image that corresponds to the otherimage's region at an infinite distance (where parallax is effectivelyzero(0)). For example, in the exemplary Janus camera system 100 of FIG.1, the overlap region corresponds to view angles larger than 180°; i.e.,at an infinite distance, both front-facing and rear-facing cameras willresolve an object on the 180 meridian identically. Artisans of ordinaryskill in the related arts will readily appreciate that this techniquecan be extrapolated for virtually any camera system; for example, asymmetric three (3) camera system would have overlap regions at 120°sectors, similarly a symmetric four (4) camera system would have overlapregions at 90° quadrants.

In one exemplary embodiment, the camera system may tag certain portionsof the source images as overlapping or non-overlapping regions; forexample, FIG. 3 illustrates one (1) exemplary file format 300 used withthe exemplary camera system 100 of FIG. 1. As shown, the exemplary fileformat 300 includes two (2) halves of the rear-facing source image, thatborder the front-facing source image. The front-facing source image is ahyper-hemispherical fisheye lens photograph; the image has annon-overlapping portion 302 which corresponds to a 165° field of view(FOV), and an overlapping portion 304 which corresponds to view anglesabove 165° to 195°. The rear-facing source image halves (right and left)have a corresponding non-overlapping 180° FOV 312 and an overlappingportion 314 which corresponds to a 195° FOV. The illustrated formatprovides a central front-facing image; other file formats may use aside-by-side format, top-bottom format, or other formatting.

The front-facing and rear-facing images are circular because theyrepresent the entire fields of view as seen by the respective cameras110, 120 (as opposed to a cropped rectangular field of view captured bya traditional camera). In an embodiment, the circular images may becaptured by using only a circular region of a respective square orrectangular image sensor. Alternatively, the circular images may becaptured using respective circular image sensors. The circular imagesmay each represent a hyper-hemispherical field of view (e.g., n degreesgreater than a hemisphere in at least one direction (referenced supra)).Thus there may be overlap in the respective fields of view near theedges of the respective images. As discussed in greater detailhereinafter, the overlap may be used to align features present withinboth of the images for stitching of these images.

In some embodiments, the camera system may change the overlap region(possibly dynamically during an ongoing video capture). For example, thecamera body itself can be modified with e.g., modular lens componentsand/or physically adjustable lens distances. In such implementations,physical capture information may be tagged with the one or more sourceimage data as e.g., metadata. Such metadata may include capturecharacteristics, including but not limited to, field of view, lensposition, focal length, optical center, camera skew, radial distortion,tangential distortion, and any other parameters which may assist indetermining the overlap region.

In other embodiments, the overlapping and non-overlapping portions ofthe image may be identified with post-capture processing. The overlapregion occurs where the source images have no noticeable parallaxeffect. Practical implementations will experience differences in captureconditions (e.g., white balance, lighting, manufacturing tolerances,lens distortion effects) which are not attributable to parallax;however, the overlap region can be detected where non-parallax baseddifferences can be identified and removed/disregarded. For example, thefront-facing and rear-facing fisheye source images can be mapped to arectilinear image, color balanced, and compared. Areas that do notexceed a threshold tolerance correspond to the overlap region. Moreover,objects that are within the overlap region and that experience parallaxcan be readily identified with such a comparison; the pixel areas thatexperience parallax can be flagged for additional processing duringsubsequent stitching efforts.

In some embodiments, the identification of overlapping ornon-overlapping regions of the source image may be identified relativeto the image dimensions. For example, a non-overlapping region may beidentified with a first radial distance from the center of the sourceimage, and the overlapping region may be identified as the area betweenthe first radial distance and a second radial distance. In otherembodiments, the non-overlapping region may be identified, and theoverlapping region encompasses everything else (or vice versa). Stillother variants may use a point other than the center point (e.g., acorner, an offset center, or some other reference point), or dimensionsor coordinate systems other than radial (e.g., Cartesian coordinates,polar coordinates).

In some implementations, the identification of overlapping ornon-overlapping regions of the source image may be defined based ongeometric shapes. For example, a non-overlapping region may beidentified with a circle, and the overlapping region may be identifiedas a ring. Other implementations may use rectangles, squares, ovals,egg-shapes, or virtually any other polygon or rounded polygon baseddefinition.

In more complex embodiments, the source images may have a hot-coded bitmap to identify overlapping or non-overlapping regions (e.g., where eachbit of the map indicates whether the corresponding pixel information isin the overlap or non-overlap region); such implementations may beparticularly useful for complex definitions (e.g., multiple cameras withdifferent view angles and/or relative positioning, moving/panning video,or other irregularly defined region). In some cases, the hot-coded bitmap may be further compressed to reduce size. While the foregoinghot-coded bit map is described, artisans of ordinary skill in therelated arts will recognize that virtually any data structure may beused with equivalent success; common examples include withoutlimitation, hash tables, arrays, multidimensional arrays, and strings.

Once the source images have been partitioned into overlapping andnon-overlapping portions, the source images can be used in a number ofsubsequent stitching, rendering, displaying, editing, or other imageprocessing tasks. For example, the overlap information may be used toassist in color balancing between front and back images. More generally,artisans of ordinary skill in the related arts will readily appreciate,given the contents of the present disclosure, that the overlap regionmay be used to assist re-encoding, re-projection image developmentand/or image post processing. In one exemplary embodiment, at step 206of the method 200, the one or more source images are stitched togetheraccording to a first stitching quality based on the overlap regioninformation.

Stitching techniques attempt to minimize parallax, lens distortion,scene motion, and exposure differences between the source images.Consider a panoramic capture under natural lighting conditions: theportion of the panorama that is between the camera and the sun will bebacklit by sunlight, whereas the portion of the panorama that is takenwith the sun to the back of the camera will be frontlit. Most camerasensors cannot capture the full range of exposure and will limit itselfto the range of exposure that provides the best fidelity. Thus thebacklit portion of the image will typically have a different exposurethan the frontlit portion. If the two images are stitched togetherwithout blending, then there will be a visible seam due to the differentexposures of the source images. Artisans of ordinary skill will readilyappreciate, given this disclosure, that a similar “seam” effect can beintroduced by different focal lengths, different color balances,different times of capture (especially where there is motion), and/orany other difference in capture parameters between the source images.

As used herein, the term “stitching” refers to the process of combiningmultiple photographic images with overlapping fields of view to producea stitched image with a substantially larger field of view, higherquality and/or improved resolution. There are a number of imagestitching techniques, and most approaches give more seamless resultswhen the overlapping regions between source images have similar captureconditions (e.g., lighting, perspective, color balance, focus.) However,some stitching techniques may be able to leverage advanced imageprocessing techniques in regions of overlap to compensate or evenbenefit from such differences; for example, image information that iscaptured under a low light exposure can be combined with imageinformation at a higher light exposure to emulate a larger dynamic rangeof exposure than would otherwise be possible with the camera sensor(also commonly referred to as High Dynamic Range (HDR) photography).Typically, an overlap region of 15%-30% of the total field of view (FOV)can be used to reconcile and blend away differences between the sourceimages to create an aesthetically “seamless” image.

One technique for quickly stitching together source images is so-called“cut-and-feather” stitching. The first step of a cut-and-feather stitchis to cut (crop out) portions of a source image that extend beyond thestitch. For example, with the Janus configuration of FIG. 1, the stitchis located at the 180 meridian. In some variants, the images may becropped to favor one of the source images. For example, a first sourceimage may have better image quality through 190° of view, therefore thecounterpart second source image is cropped at a corresponding 170°.Moreover, since quality may vary over the image, it is furtherappreciated that different portions of a source image may bepreferentially weighted. For example, a first source image may have abetter or worse image quality in a certain subset of the overlap (andtreated with a larger or smaller cropping area), whereas the remainingportions of the image are cropped at the default (e.g., 180°).

The resulting cropped images are joined and “feathered.” Here,feathering generally refers to, without limitation: blending filtering,blurring, sharpening, burning, and/or any number of other imageprocessing techniques. More generally, feathering reduces or obfuscatesthe seam by averaging the differences in pixel values across the seam.Feathering is limited in effectiveness because it only considers thepixel information of the source images, and may introduce someundesirable artifacts into the resulting image (e.g., ghosting,translucency, etc.) However, feathering is computationally simple andcan be performed with very little processing effort (and can beperformed in varying degrees). Feathering is suitable for use on mostmobile platforms, and/or where stitching must be done quickly (e.g.,streaming video).

Cut operations and feather operations are well understood by artisans ofordinary skill; for example, additional details for cutout and feathertype operations are described within “Image Alignment and Stitching: ATutorial,” preliminary draft published Sep. 27, 2004 to RichardSzeliski, incorporated herein by reference in its entirety. Still othertechniques and/or variations may be made by artisans of ordinary skillin the related arts, the foregoing being purely illustrative.

In some implementations, a cut-and-feather stitch may also provide someinformation as to the degree of confidence of the stitch. Simple metricsmay include, without limitation: the differences in pixel information atthe seam prior to feathering (e.g., a sum of difference, or sum ofsquare of difference, or other metric), the amount of feathering (e.g.,a sum of changes to pixel values), and/or other quantitative measures ofsmoothing. More complicated metrics may include e.g., user identifiedartifacts, holistic measures of the image (including portions outside ofthe stitch), and/or other

Various other techniques for stitching images may be used consistentwith the present disclosure, the foregoing being purely illustrative.Common examples of such techniques include without limitation: cut andfeather stitching, depth based stitching, and multi-band stitching.

In some embodiments, the overlap region includes redundant informationfrom multiple source images. For example, in a six (6) sided cube systemcamera, the corners of the cube will capture three (3) distinct vantages(e.g., a left, a right, and a top perspective). Still other camerasystems may incorporate stereo vision (e.g., two or more lensesproviding a stereo view) for use in e.g., 3D video and photography. Inanother example, a panning video capture can be divided into individualframes, and then stitched together. Video capture embodiments may usemany frames (at many different perspectives) to perform stitching.Additionally, while the following description is presented within thecontext of visible light, other forms of image capture may be used withequivalent success. Common examples include without limitation,infrared, sonic, radar, lidar, and/or any other form of capture. In somecases, different capture technologies can provide a diversity ofinformation more easily than visual imagery. For example, asonar/visible light hybrid system can provide depth information andvisible light information.

Various different stitching quality metrics may be gathered. Stitchingmetrics may be based on the original quality of the source images e.g.,a blurry or under exposed image provide considerably less informationduring stitching. Stitching metrics may also quantify the differencesbetween the original source images at the stitch (e.g., the amount ofdisparity at the stitch, larger disparities result in poorer results).Under such measures, the difference in the stitch may be quantified inthe amount of adjustment to pixel values; e.g., larger shifts in colorvalues may indicate poor stitching. In other measures, the difference inthe stitch may be quantified by the absolute number of pixels whichchanged (rather than a measure of pixel information). Additionally,changes to source images may be weighted differently. For example, asource image with dubious image quality (e.g., due to underexposure) mayhave an underweight effect on the resulting stitch.

Still other stitching metrics may quantify holistic differences betweenthe post-stitch image and its original source images. Stitching that haswarping or skewing that appears to be multimodal (with multiplemaxima/minima) is unlikely to be attributable to mere differences inperspective and are more likely due to an unintentional “fold-in” stitch(where falsely matched distinct features of the source images were“folded” together into one feature). Similarly, excessive warping orskewing is also undesirable and may indicate problems with theoriginally captured source images. Some stitching metrics may attempt toquantify undesirable artifacts (e.g., blurriness, sharpness, unusualcoloration). More generally, artisans of ordinary skill in the relatedarts will readily appreciate that virtually any “confidence” metric canbe used to convey the quality of the stitched result.

Additionally, it should be recognized that some metrics are specific toa particular application. For example, a video that is constructed fromstitched images may experience unusual effects as objects pass throughthe seam. The individual stitched images may be well stitched on a frameby frame basis; however, when viewed together, an object with a constantvelocity may appear to “bump” through the seam. In some cases, suchmotion may be reflected in subsequent encoding of the resulting video(e.g., motion vectors that are generated during e.g., MPEG encoding).Similarly, in some cases, video of fast moving objects may be moreaesthetically pleasing if allowed to retain motion blur rather thanbeing unnaturally “sharpened”. Also, 3D applications may need to retainparallax effects and/or focal distance blurring so as to allow for“natural” depth perception. Other application specific considerationswill be made apparent to those of ordinary skill in the related arts,given the contents of the present disclosure.

Stitching quality may be localized to a spatial location within thestitched image. For example, consider an object that is occluded fromone source image but present in the other. The remaining portions of thestitched image may be well stitched; however, the object itself will bedifficult to reconcile into a final stitched image (e.g., whether theobject is present or not). Accordingly, the resulting stitched image canidentify a low confidence metric that is specific to the location of theobject. In this manner, during post-processing, a user could selectivelychoose to render the obscured object or edit the object out. In arelated example, stitching quality may be localized to a temporallocation within video frames of images. For example, consider an objectthat moves at different speeds or that frequently changes direction. Thestitched frames may each be well stitched; however, when encoded into avideo format, the motion may be irregular and/or jumpy. Stitching thatresults in unusual motion encoding may imply a lower quality stitch.

Additionally, stitching techniques may be localized to a spatiallocation within the stitched image. For example, a mobile device may beable to perform more complex stitching techniques (e.g., a depth-basedstitch) over certain portions of interest within the image, but usesimpler stitching techniques (e.g., a cut-and-feather stitch) over areaswhich are less important. Selective stitching can be useful to providebetter results in reasonable processing times within the constraints ofa mobile platform. For example, the user may be able to select portionsof the quickly stitched image to check “on-the-spot”; the resultinglocalized high quality stitching will let the user know whether they“got the shot”.

At step 208 of the method 200, the resulting stitched image and at leastthe overlapping portions of the source images are saved. In someembodiments, the resulting stitched image and the entire original sourceimages are preserved. In other embodiments, only the correspondingoverlap regions of each of the original source images are preserved. Instill other embodiments, only the low confidence portions of the overlapregions are retained.

In one such variant, stitching metadata is also preserved. Metadata mayinclude information such as, but not limited to: camera system and/orcapture parameters (e.g., exposure, lens type, camera orientations,fields of view, and/or other such information), a reference to thepixels that were stitched (e.g., bit maps, pointers, or other datastructures), indicia of stitching techniques, projection information,useful stitching metrics (e.g., difference metrics, confidence metrics,warp metrics, skew metrics), user tagged information, and/or any otherpost processing information.

FIG. 4 illustrates one (1) exemplary file format 400 representing astitching of the exemplary file format 300 of FIG. 3. As shown, theexemplary file format 400 includes two (2) halves of the rear-facingnon-overlapping regions 312, that border the front-facingnon-overlapping region 302. The resulting stitching area 402 is at afirst stitch quality. Additionally, the illustrated data structureprovides the original front-facing and rear-facing overlap region data(304, 314) as well as stitching metadata 404. Artisans of ordinary skillin the related arts will readily appreciate that the data structureshown in FIG. 4 is purely illustrative and does not represent the actualproportions of the data structure; e.g., the stitching data for acut-and-feather operation would be small relative to the non-overlappingimage data.

The data structure of FIG. 4 is composed of multiple substituent datastructures. The various substituent data structures may be compressedand/or formatted to varying degrees. For example, artisans of ordinaryskill in the related arts will appreciate that image compression is wellunderstood in the related arts; thus the rear-facing non-overlappingregions 312, front-facing non-overlapping region 302, and resultingstitching area 402 can be compressed according to any number of existingpanoramic image formats. The rear-facing overlapping region 314 andfront-facing overlapping region 304 may be compressed as well; however,irregularly shaped overlapping regions may not be well suited fortraditional image compression (and may be compressed according to moregeneric data compression formats).

Additionally, there may be a high degree of similarity between largeportions of the rear-facing overlapping region 314 and front-facingoverlapping region 304 that can be used to greatly facilitatecompression. More directly, overlap regions that have a large focaldistance will not experience parallax. Accordingly, these portions ofthe data structure will be very similar (after accounting for variationsin exposure or other mild differences) and can be heavily compressed.The stitching metadata may be compressed or uncompressed, depending onits content (e.g., benefits for compressing small amounts of data may benegligible).

FIG. 5 illustrates a generalized method for re-stitching a previouslystitched image according to a second stitching quality.

At step 502, an image processing system obtains at least a portion ofthe previously stitched image at a first stitching quality. In someembodiments, a data structure that includes the entire stitched image isprocured. For example, the image processing system may receive apreviously stitched image stored within a data structure (such as theexemplary data structure described in FIG. 4, described supra.) In otherembodiments, only a portion of the stitched image is received. Forinstance, only a viewport into a stitched panorama may be rendered (thesource data structure may remain in e.g., a server, other cloud basedstorage); viewport based rendering is particularly useful for limitedbandwidth applications via a streaming video feed (e.g., for a virtualreality (VR) headset).

In some variants, the image processing system determines whether or notthe previously stitched image should be re-stitched. As previouslynoted, there may circumstances where the original stitched image isacceptable; e.g., where the original stitching has a high confidencemetric, or where stitching artifacts may not be apparent to the viewer(e.g., a fast moving video application, poor lighting conditions).Accordingly, the image processing system may first determine whetheradditional stitching is required. In some cases, the image processingsystem receives user instruction (e.g., via a graphical user interface(GUI)) as to whether the image should be re-stitched.

In some cases, a human user may identify areas of the image that must bere-stitched. For example, the user may be able to identify a feature andits ghost to be merged (or false ghost); this information can be used ina re-stitching process to determine depth and/or perspective. In othercases, a human user may identify temporal portions of a video thatshould be smoothed, blurred, sharpened or otherwise post-processed. Inhybrid editing systems, the image processing system may identify (basedon the stitching metadata), areas where the first stitching process hadparticular difficulty or which are likely to be incorrect (e.g., areaswhich had a low confidence metrics or where the original source captureswere of poor quality). The user can then provide additional information.For instance, an initial stitching may not have been able to reconcilewhether an object should or should not have been included in the image(such as may occur where e.g., the image was in a one camera's blindspot but not the other). The image processing software can identify theresulting area, and allow the user to select whether the object shouldbe included or not.

In some embodiments, the image processing system may rely on theoriginal projection and/or merely re-stitch the same stitch line. Inmore sophisticated embodiments, the image processing system may select anew desired projection (step 504) and/or a new re-stitch line (step506). As a brief aside, the initial stitching of the source imagesdescribed supra, was performed according to a specific projection thatwas defined by the physical parameters of the camera system; e.g., theJanus camera configuration of FIG. 1 uses fisheye lenses which create aspherical projection. One salient benefit of re-stitching from apreviously stitched image is that the physical attributes of a camerasystem are no longer a limitation. The projection and the re-stitch linedetermine the area of an image that is reproduced with the highestfidelity and most consistent perspective, and also which areas of theimage can be acceptably distorted. Consequently, the ability to changeprojections and/or adjust placement of a stitch line may be used tosubstantially improve the quality of the initial first stitching (onesuch example is described in greater detail hereinafter, see e.g.,Exemplary Post-processing Projection and Stitching).

In one exemplary embodiment, re-stitching may be done to warp a firstprojection into a second projection. Commonly used projections includewithout limitation e.g., spherical, equirectangular, rectilinear, and/orstereographic projections; artisans will readily appreciate thatvirtually any projection of the image may be substituted with equivalentsuccess, given the contents of the present disclosure. Differentprojections can be used with a variety of different applications or toachieve certain artistic or otherwise aesthetic effects. More directly,footage that has been captured with e.g., an action camera, does nothave to be used in an action camera-like application. For example, aspherical image can be remapped over to a 2D poster blow-ups and/or anynumber of other potential uses.

In one exemplary embodiment, the stitch line can be re-defined. Forinstance, more or less of the overlap region can be used from each ofthe source images. In some variants, a user or stitching algorithm mayshift the stitching line to favor one of the source images over theother during post processing. This may allow for a jagged or irregularlydefined stitching line; such as may be useful to include (or exclude)objects that are hidden in a blind spot for one of the cameras.Similarly, a previously stitched image may have falsely matched distinctfeatures of the source images and obscured the intervening image data by“folding” the two features together into one feature. The interveningdata can be recovered from the “fold-in” region by revealing theoriginal overlap region, and correcting the stitch line. In such cases,the overlap data surrounding a particular stitch line (or portionthereof) may be recovered from the source images, and the user oralgorithm may re-draw the stitch line through the overlap region.

Those of ordinary skill in the related arts may additionally recognizethat steps 504 and 506 may only affect a portion of the originallystitched image; thus some implementations may leave the remaining areasof the stitched image undisturbed. Alternatively, the entire image maybe re-stitched so as to e.g., operate with legacy stitching acceleratorsor algorithms.

At step 508 of the method 500, the post-processor retrieves theoverlapping portions of the images corresponding to at least there-stitch area. In some embodiments, the post-processor may retrieve theentire overlapping area, and select only those portions that are beingre-stitched. In other embodiments, the post-processor may identify theportions that are being re-stitched and only retrieve the correspondingoverlapping areas.

In some cases, the post-processor may not be able perform a re-stitchfor any number of other considerations e.g., existing processor burdens,power consumption limitations, streaming time interval limitations,other network congestion. For example, a streaming VR headset may allowfor re-stitching of data on-the-fly for a streaming application; theoriginally stitched images may be re-stitched where the user is viewingscenery at a leisurely rate; however, when the user is rapidly moving,re-stitching may not be possible within the streaming refresh rate (andlikely imperceptible to the user anyway). Consequently, some embodimentsmay merely “pass-through” the previously stitched image where thepost-processor is unable to complete the re-stitch within its otherconsiderations.

At step 510 of the method 500, the post-processor re-stitches the imagewhere it has the overlapping portions of the images corresponding to atleast the re-stitch area. In one exemplary embodiment, the re-stitchingis based on a higher quality stitching technique than the initialstitch. More complex stitching techniques analyze the source images tointelligently determine how to best combine them. Such techniques mayinclude e.g., feature detection to assist in alignment, depth perceptionto correct shifts in perspective, motion prediction to reduce ghostingartifacts and blur, edge detection to resolve differences in focus,color matching to correct differences in exposure and lighting, and/orany number of other image recognition techniques. These stitchingtechniques may be computationally complex, and generally are moredifficult (possibly impractical) to be performed on mobile platforms.

One such example of a complex stitching technique is so-calleddepth-based stitching which uses object/feature detection and/orstereovision, to identify objects of varying distance or “depth” fromthe camera system. Based on the inferred depth of the source images andthe relative distance and field of views of the corresponding camerasystem, the effects of parallax can be reduced or removed entirely.Existing solutions for removing parallax may be widely found throughrelevant literature; for example, the study of isomorphism withinprojective space (e.g., two equivalent objects) that are induced by anisomorphism of a vector space (e.g., two equivalent lines of sight) isbroadly described as “homography”.

Complex stitching techniques generally include one or more steps of: (i)determining which pixel coordinates of a first source image correspondto pixel coordinates of another image (alignment), (ii) reconcilingdifferences in redundant pixel values between the two source images,(iii) applying blending filters, and (iv) warping the resulting stitchedimage to a projection. Various other techniques may be used, thefollowing descriptions being purely illustrative.

Feature based detection can be based on edge detection, surfacedetection, object recognition, shape recognition, and/or any number ofother visual recognition techniques. Additionally, artisans of ordinaryskill in the related arts will readily appreciate that any featurematching between two different images, no matter how similar, will haveambiguous features or features which cannot be matched; consequently,most feature detection algorithms can only achieve a best fit based one.g., a sum of differences, sum of squares, or other such metric. Suchmetrics can also be reported as a confidence metric.

Moreover, as will be further recognized, there are many ways to adjustan image to correct for e.g., parallax and/or blind spots. Adjustmentsmay include, without limitation: warping, blurring or sharpening,selection, and/or averaging pixel information from the source images.For example, changing the perspective of a field of view may beperformed by warping or skewing the corresponding field of view.Moreover, identified objects at different distances may be skewed todifferent degrees corresponding to their distance. Blurring and/orsharpening may be used so to consistently render an object at aparticular focal distance. For example, an object that is blurred in onesource image but sharp in the other source image (due to different focaldistances), may be blurred or sharpened in the aggregate, to give aconsistent focal distance. Similarly, objects that are in one sourceimage but not the other (e.g., such as where only one of the cameras hasa blind spot) will create a “ghost” or translucent version, if theredundant pixels are directly combined. In such cases, a more accuraterendering can be performed by selecting the pixels from one source imageor the other. In still other cases, ghosting or translucency may be adesirable artifact. For example, objects in motion look more naturalwhen rendered with slight ghosting, rather than with crisp lines (crispedges create a stop-motion effect).

Feature recognition based image stitching operations are well understoodby artisans of ordinary skill; for example, additional details forfeature based stitching operations are described within “Image Alignmentand Stitching: A Tutorial,” preliminary draft published Sep. 27, 2004 toRichard Szeliski, previously incorporated herein by reference in itsentirety. Still other techniques and/or variations may be made byartisans of ordinary skill in the related arts, the foregoing beingpurely illustrative.

Various other techniques for re-stitching images may be used consistentwith the present disclosure, the foregoing being purely illustrative.

Exemplary Post-Processing Projection and Stitching

As previously alluded to, post-process re-stitching allows a subsequentimage processing system to change projections and/or adjust placement ofa stitch line to substantially improve the quality of the initial firststitching. FIG. 6 illustrates an exemplary initial stitching of aspherical projection 602 having a meridian 612 that can be re-stitchedto a cylindrical projection 604A, 604B with an equator 614 or a meridian612. Initially, the first and second circular hemispheres are stretched(a homographic projection) to a corresponding equirectangular half and asecond equirectangular half respectively. Specifically, the circularimages may each be stretched horizontally to fill a square. As a resultof this projection, the equirectangular images may become increasinglymore distorted as the top and bottom edges are approached. For example,the center row of pixels may not undergo any stretching during theequirectangular projection processing, while the top and bottom row inthe original circular image (which may each be represented by arespective single pixel) may be stretched to fill the entire top andbottom rows respectively of the equirectangular projections.

Referring now to cylindrical projection 604A, each equirectangular imageincludes an image representing a 180° field of view along the verticalaxis and a 180° field of view along the horizontal axis of thehemispherical projections. In some embodiments, the original overlapinformation (which was not part of the stitched image) may also beretrieved to assist in stretching; e.g., the stretched images represent180°+2n where n represents the degree of overlap between the respectivefields of view of the original images. For example, the firstequirectangular image may include a field of view in the range of 0°−nto 180°+n and the second equirectangular image may include a field ofview in the range of 180°−n to 360°+n along the horizontal axis of eachhemispherical projection. As a brief aside, the distortion introduced atthe top and bottom of the respective images is primarily introduced as aresult of the projection from a spherical image onto an equirectangularimage. Although ultra-wide-angle lens (e.g., a so-called fisheye lens)introduces a characteristic distortion into the captured image, thischaracteristic distortion can be subsequently removed from the generatedspherical image because this characteristic distortion is generallyknown (i.e., fixed within a reasonable degree of accuracy) when using anultra-wide-angle lens of appreciable quality.

In a first variant, the stretched halves may be re-stitched at thestretched meridians 612; e.g., the left image is stitched to the rightimage by aligning one or more features appearing in the n overlappingregion along the meridian 612. The resulting image 604 (referred toherein as an “equatorial view” of the spherical image) provides anequirectangular projection of the spherical field of view. Thisorientation of the spherical image may be useful because many existingviewing and editing applications for spherical images assume that animage is received in this orientation. During this step, the disclosedembodiments can additionally retrieve the original overlap region fromthe source images; this enables the post-processor to improve or removeartifacts that were present in the original stitching. In other words,the post-processor is not limited to the previously stitched image data.

One advantage of meridian stitching is that a conventional stitchingalgorithm designed for rectangular images can be used without requiringa specialized stitching algorithm for operating directly in thespherical domain. More directly, existing stitching algorithms weredesigned for stitching together multiple rectilinear images, and thusdeliver the best results when the image is rectilinear. However, apotential problem with the above-described process is that the top andbottom of the stitch lines in the stitched image correspond to portionsof the equirectangular images that were subject to the greatestdistortion from the spherical to equirectangular conversion process.This can lead to various errors in the stitching algorithm, which canresult in undesirable artifacts near the top and bottom edges of thestitch lines. More directly, the upper and lower limits of meridianstitching experiences the worst case projection distortions and islikely to introduce undesirable stitching artifacts.

In a second variant, the spherical halves may be stretched into anequirectangular projection as an equatorial stitch. More directly,rather than stretching the periphery of the fisheye capture (wheredistortion is the worst), the center of the fisheye capture can bestretched, thereby maintaining as much fidelity at the edges (where thestitch occurs) as possible. In other words, the first circular image andthe second circular image may be projected to a first rectangular imageand a second rectangular image where the outer edge of the circularimages maps to the equator of the final equirectangular projection andthe center point of the circular images have been stretched. Thisprojection may also be understood as taking increasing concentric ringsof pixels from the circular images and arranging them in rows (e.g.,forming a triangle in which the center point represents the vertex ofthe triangle and the outer most ring represents the base of thetriangle); this triangle can then be stretched to create a rectangle. Inthis projection, the distortion in the rectangular image due tostretching of the pixels increases near the top edge of the firstrectangular image (which corresponds to the center of the fisheyeimage). Particularly, along the top edge, a single pixel (representingthe center point) may be stretched across the entire top edge, while thebottom edge experiences no stretching. The stretching of pixel valuescan be handled any number of ways, including padding (repeating the samepixel value), averaging (interpolating pixel values as weighted averagesof source pixel values) or a polynomial fit (interpolating pixel valueswith polynomial fitting). The resulting rectangular images represent afield of view from 0°−n to 90° along the vertical axis (corresponding tothe angles from the outer edge to the center point of the originalhemispherical projection) and from 0° to 360° along the horizontal axis(corresponding to the angles around the circumference of the originalhemispherical projection).

The first rectangular image and the second rectangular image may then bestitched together (e.g., by aligning the n overlapping degrees at theequator 614). The resulting image may be referred to as “polar view” ofa spherical image. The stitch line is referred to herein as an“equatorial stitch” because the stitched edges correspond to the equator614 between the two hemispheres captured by the cameras 110, 120. As canbe seen, in contrast to the meridian stitch described above, only asingle stitch line is used (compared to two stitch lines) and the stitchis performed along edges of the images that were minimally distortedduring the rectangular projection process. Furthermore, the postprocessor can also retrieve the original overlap region from the sourceimages to further improve stitching quality. As a result, stitchingartifacts caused by the projection distortion are greatly reduced oreliminated.

Unfortunately, even though the polar view can greatly reduce stitchingerror, the polar view is undesirable for viewing. Consequently, once theimages have been stitched in the polar view, the resulting stitchedimage is converted back to a more desirable projection for viewing. Forexample, FIG. 7 illustrates one such process. As shown, the stitchedimage 702 is divided at into left and right equally sized sub-images,704. The left sub-image 706 may be rotated clockwise 90° and the rightsub-image 708 may be rotated counterclockwise 90°. The rotated leftsub-image 706 and the rotated right sub-image 708 may be re-combined(e.g., by aligning the right edge of the rotated left sub-image 706 withthe left edge of the rotated right sub-image 708) to generate there-orientated image 710. Finally, the re-oriented image 710 can betransformed to any number of other projections (e.g., equirectangular(cylindrical), cubic, octahedral, and/or spherical projection) view byany number of well-known linear transforms and/or image warpingtechniques.

While the foregoing example is presented within the context of the Janustwo (2) camera configuration described within FIG. 1, artisans ofordinary skill in the related arts will readily appreciate that thetechniques described therein may be broadly applied to a variety ofother camera systems (e.g., camera systems having three (3) or morecameras). Still other optimizations and techniques for adjustingplacement of a stitch line to substantially improve the quality of astitching are described within co-owned and co-pending U.S. patentapplication Ser. No. 15/289,851, filed Oct. 10, 2016, and entitled“Apparatus and Methods for the Optimal Stitch Zone Calculation of aGenerated Projection of a Spherical Image,” the foregoing applicationbeing incorporated herein by reference in its entirety.

Exemplary Apparatus —

FIG. 8 is a block diagram illustrating components of an examplecomputing system able to read instructions from a computer-readablemedium and execute them in one or more processors (or controllers). Thecomputing system in FIG. 8 may represent an implementation of, forexample, the video processing device for performing the stitching and/orre-stitching processes described herein.

The computing system 800 can be used to execute instructions 824 (e.g.,program code or software) for causing the computing system 800 toperform any one or more of the methodologies (or processes) describedherein. In alternative embodiments, the computing system 800 operates asa standalone device or a connected (e.g., networked) device thatconnects to other computer systems. The computing system 800 mayinclude, for example, a personal computer (PC), a tablet PC, a notebookcomputer, or other device capable of executing instructions 824(sequential or otherwise) that specify actions to be taken. In anotherembodiment, the computing system 800 may include a server. In anetworked deployment, the computing system 800 may operate in thecapacity of a server or client in a server-client network environment,or as a peer device in a peer-to-peer (or distributed) networkenvironment. Further, while only a single computer system 800 isillustrated, a plurality of computing systems 800 may operate to jointlyexecute instructions 824 to perform any one or more of the methodologiesdiscussed herein.

The example computing system 800 includes one or more processing units(generally processor apparatus 802). The processor apparatus 802 mayinclude, for example, a central processing unit (CPU), a graphicsprocessing unit (GPU), a digital signal processor (DSP), a controller, astate machine, one or more application specific integrated circuits(ASICs), one or more radio-frequency integrated circuits (RFICs), or anycombination of the foregoing. The computing system 800 also includes amain memory 804. The computing system 800 may include a storage unit816. The processor 802, memory 804 and the storage unit 816 maycommunicate via a bus 808.

In addition, the computing system 800 may include a static memory 806, adisplay driver 810 (e.g., to drive a plasma display panel (PDP), aliquid crystal display (LCD), or a projector). The computing system 800may also include input/output devices, e.g., an alphanumeric inputdevice 812 (e.g., touch screen-based keypad or an external input devicesuch as a keyboard), a dimensional (e.g., 2-D or 3-D) control device 814(e.g., a touch screen or external input device such as a mouse, atrackball, a joystick, a motion sensor, or other pointing instrument), asignal capture/generation device 818 (e.g., a speaker, camera, and/ormicrophone), and a network interface device 820, which also areconfigured to communicate via the bus 808.

Embodiments of the computing system 800 corresponding to a client devicemay include a different configuration than an embodiment of thecomputing system 800 corresponding to a server. For example, anembodiment corresponding to a server may include a larger storage unit816, more memory 804, and a faster processor 802 but may lack thedisplay driver 810, input device 812, and dimensional control device814. An embodiment corresponding to an action camera may include asmaller storage unit 816, less memory 804, and a power efficient (andslower) processor 802 and may include multiple camera capture devices818.

The storage unit 816 includes a computer-readable medium 811 on which isstored instructions 824 (e.g., software) embodying any one or more ofthe methodologies or functions described herein. The instructions 824may also reside, completely or at least partially, within the mainmemory 804 or within the processor 802 (e.g., within a processor's cachememory) during execution thereof by the computing system 800, the mainmemory 804 and the processor 802 also constituting computer-readablemedia. The instructions 824 may be transmitted or received over anetwork via the network interface device 820.

While computer-readable medium 822 is shown in an example embodiment tobe a single medium, the term “computer-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 824. The term “computer-readable medium” shall also betaken to include any medium that is capable of storing instructions 824for execution by the computing system 800 and that cause the computingsystem 800 to perform, for example, one or more of the methodologiesdisclosed herein.

Where certain elements of these implementations can be partially orfully implemented using known components, only those portions of suchknown components that are necessary for an understanding of the presentdisclosure are described, and detailed descriptions of other portions ofsuch known components are omitted so as not to obscure the disclosure.

In the present specification, an implementation showing a singularcomponent should not be considered limiting; rather, the disclosure isintended to encompass other implementations including a plurality of thesame component, and vice-versa, unless explicitly stated otherwiseherein.

Further, the present disclosure encompasses present and future knownequivalents to the components referred to herein by way of illustration.

As used herein, the term “computing device”, includes, but is notlimited to, personal computers (PCs) and minicomputers, whether desktop,laptop, or otherwise, mainframe computers, workstations, servers,personal digital assistants (PDAs), handheld computers, embeddedcomputers, programmable logic device, personal communicators, tabletcomputers, portable navigation aids, J2ME equipped devices, cellulartelephones, smart phones, personal integrated communication orentertainment devices, or literally any other device capable ofexecuting a set of instructions.

As used herein, the term “computer program” or “software” is meant toinclude any sequence or human or machine cognizable steps which performa function. Such program may be rendered in virtually any programminglanguage or environment including, for example, C/C++, C#, Fortran,COBOL, MATLABT™, PASCAL, Python, assembly language, markup languages(e.g., HTML, SGML, XML, VoXML), and the like, as well as object-orientedenvironments such as the Common Object Request Broker Architecture(CORBA), Java™ (including J2ME, Java Beans), Binary Runtime Environment(e.g., BREW), and the like.

As used herein, the terms “integrated circuit”, is meant to refer to anelectronic circuit manufactured by the patterned diffusion of traceelements into the surface of a thin substrate of semiconductor material.By way of non-limiting example, integrated circuits may include fieldprogrammable gate arrays (e.g., FPGAs), a programmable logic device(PLD), reconfigurable computer fabrics (RCFs), systems on a chip (SoC),application-specific integrated circuits (ASICs), and/or other types ofintegrated circuits.

As used herein, the term “memory” includes any type of integratedcircuit or other storage device adapted for storing digital dataincluding, without limitation, ROM. PROM, EEPROM, DRAM, Mobile DRAM,SDRAM, DDR/2 SDRAM, EDO/FPMS, RLDRAM, SRAM, “flash” memory (e.g.,NAND/NOR), memristor memory, and PSRAM.

As used herein, the term “processing unit” is meant generally to includedigital processing devices. By way of non-limiting example, digitalprocessing devices may include one or more of digital signal processors(DSPs), reduced instruction set computers (RISC), general-purpose (CISC)processors, microprocessors, gate arrays (e.g., field programmable gatearrays (FPGAs)), PLDs, reconfigurable computer fabrics (RCFs), arrayprocessors, secure microprocessors, application-specific integratedcircuits (ASICs), and/or other digital processing devices. Such digitalprocessors may be contained on a single unitary IC die, or distributedacross multiple components.

As used herein, the term “camera” may be used to refer withoutlimitation to any imaging device or sensor configured to capture,record, and/or convey still and/or video imagery, which may be sensitiveto visible parts of the electromagnetic spectrum and/or invisible partsof the electromagnetic spectrum (e.g., infrared, ultraviolet), and/orother energy (e.g., pressure waves).

It will be recognized that while certain aspects of the technology aredescribed in terms of a specific sequence of steps of a method, thesedescriptions are only illustrative of the broader methods of thedisclosure, and may be modified as required by the particularapplication. Certain steps may be rendered unnecessary or optional undercertain circumstances. Additionally, certain steps or functionality maybe added to the disclosed implementations, or the order of performanceof two or more steps permuted. All such variations are considered to beencompassed within the disclosure disclosed and claimed herein.

While the above detailed description has shown, described, and pointedout novel features of the disclosure as applied to variousimplementations, it will be understood that various omissions,substitutions, and changes in the form and details of the device orprocess illustrated may be made by those skilled in the art withoutdeparting from the disclosure. The foregoing description is of the bestmode presently contemplated of carrying out the principles of thedisclosure. This description is in no way meant to be limiting, butrather should be taken as illustrative of the general principles of thetechnology. The scope of the disclosure should be determined withreference to the claims.

What is claimed:
 1. An apparatus configured to stitch source imagesaccording to a first stitching quality, the apparatus comprising: two ormore cameras characterized by two or more corresponding fields of view(FOVs), the two or more corresponding FOVs being characterized by atleast one overlapping region; a processor apparatus; and anon-transitory computer readable medium in data communication with theprocessor apparatus and comprising one or more instructions which whenexecuted by the processor apparatus, cause the apparatus configured tostitch source images to: obtain two or more images from the two or morecameras; identify the at least one overlapping region of the obtainedtwo or more images; post-process the obtained two or more images tocreate a post-processed image in accordance with the first stitchingquality having a first confidence metric; and store the post-processedimage and one or more information associated with the identified atleast one overlapping region, where the one or more informationassociated with the identified at least one overlapping region enables are-stitch of the obtained two or more images in accordance with a secondconfidence metric, the second confidence metric being different than thefirst confidence metric.
 2. The apparatus of claim 1, wherein theidentified at least one overlapping region of the obtained two or moreimages are identified based on a physical orientation of at least oneof: (i) the two or more cameras, and/or (ii) the two or more FOVs. 3.The apparatus of claim 1, wherein the identified at least oneoverlapping region of the obtained two or more images are identifiedbased on one or more shared features detected within the obtained two ormore images.
 4. The apparatus of claim 1, wherein the post-processcomprises a cut-and-feather stitch of the obtained two or more images.5. The apparatus of claim 1, wherein the post-process comprises adepth-based stitch of the obtained two or more images.
 6. The apparatusof claim 1, wherein the stored one or more information comprises datarepresentative of stitching confidence metrics.
 7. The apparatus ofclaim 1, wherein the stored one or more information comprises originalpixel information associated with the at least one overlapping region.8. The apparatus of claim 1, wherein the two or more image capturecomponents comprise a first camera facing in a front orientation, and asecond camera facing in a rear orientation.
 9. The apparatus of claim 8,wherein the two or more cameras are characterized by corresponding twoor more hyper-hemispherical FOVs.
 10. A method for stitching sourceimages according to a first stitching quality, the method comprising:obtaining two or more source images from two or more cameras;identifying at least one overlapping region of the obtained two or moresource images; post-processing the obtained two or more source images tocreate a post-processed image in accordance with the first stitchingquality having a first confidence metric; and storing the post-processedimage and information associated with the identified at least oneoverlapping region, the information associated with the identified atleast one overlapping region enabling a re-stitching of the obtained twoor more source images in accordance with a second confidence metric, thesecond confidence metric being different than the first confidencemetric.
 11. The method of claim 10, further comprising displaying atleast a portion of the post-processed image.
 12. The method of claim 11,wherein the post-processing further comprises determining a projectionto be used in the displaying.
 13. The method of claim 12, furthercomprising stitching at least a portion of the post-processed imagebased on the determined projection.
 14. The method of claim 11, whereinthe post-processing further comprises determining a viewport to display.15. The method of claim 14, further comprising stitching at least aportion of the post-processed image based on the determined viewport.16. The method of claim 11, wherein the post-processing furthercomprises (i) determining one or more device resource limitations, and(ii) configuring one or more algorithmic operations of thepost-processing based at least in part on the determined one or morelimitations.
 17. The method of claim 14, further comprising performingonly a partial stitching of at least a portion of the post-processedimage based at least in part on the determined one or more deviceresource limitations.
 18. An apparatus configured to re-stitch an imagecharacterized by a first stitching quality to a second stitchingquality, the apparatus comprising: a network interface; a processorapparatus in data communication with the network interface; and storageapparatus in data communication with the processor apparatus andcomprising a non-transitory computer readable medium having one or moreinstructions which are configured to, when executed by the processorapparatus, cause the apparatus configured to re-stitch to: obtain atleast a portion of the image characterized by the first stitchingquality, the first stitching quality characterized by a first confidencemetric; determine a region of the at least the portion of the image tobe re-stitched to a second stitching quality, the second stitchingquality characterized by a second confidence metric, the secondconfidence metric being greater than the first confidence metric; obtaininformation associated with the determined region; and re-stitch theregion at the second stitching quality based at least in part on theobtained information.
 19. The apparatus of claim 18, where thedetermined region corresponds to at least one overlapping region of twoor more source images.
 20. The apparatus of claim 19, where the obtainedone or more information associated with the determined region comprisesat least one of: (i) stitching confidence metrics data, and (ii)original capture information data from the two or more source images.